Overview

Dataset statistics

Number of variables16
Number of observations20030
Missing cells24884
Missing cells (%)7.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory128.0 B

Variable types

Numeric10
Categorical5
Unsupported1

Alerts

name has a high cardinality: 19503 distinct valuesHigh cardinality
host_name has a high cardinality: 5915 distinct valuesHigh cardinality
last_review has a high cardinality: 1202 distinct valuesHigh cardinality
latitude is highly overall correlated with neighbourhoodHigh correlation
longitude is highly overall correlated with neighbourhoodHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
neighbourhood is highly overall correlated with latitude and 1 other fieldsHigh correlation
room_type is highly imbalanced (52.1%)Imbalance
neighbourhood_group has 20030 (100.0%) missing valuesMissing
last_review has 2406 (12.0%) missing valuesMissing
reviews_per_month has 2406 (12.0%) missing valuesMissing
price is highly skewed (γ1 = 25.44770219)Skewed
minimum_nights is highly skewed (γ1 = 56.36139206)Skewed
name is uniformly distributedUniform
id has unique valuesUnique
latitude has unique valuesUnique
longitude has unique valuesUnique
neighbourhood_group is an unsupported type, check if it needs cleaning or further analysisUnsupported
number_of_reviews has 2388 (11.9%) zerosZeros
availability_365 has 9344 (46.7%) zerosZeros

Reproduction

Analysis started2023-05-04 20:41:57.041415
Analysis finished2023-05-04 20:42:16.129619
Duration19.09 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct20030
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15417254
Minimum2818
Maximum30580413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:16.284342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2818
5-th percentile1355640.1
Q18188422.8
median15630490
Q322025770
95-th percentile29148116
Maximum30580413
Range30577595
Interquartile range (IQR)13837348

Descriptive statistics

Standard deviation8569403.6
Coefficient of variation (CV)0.55583203
Kurtosis-1.0603338
Mean15417254
Median Absolute Deviation (MAD)6836429
Skewness-0.049708236
Sum3.088076 × 1011
Variance7.3434678 × 1013
MonotonicityStrictly increasing
2023-05-04T22:42:16.458605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2818 1
 
< 0.1%
19981710 1
 
< 0.1%
19989602 1
 
< 0.1%
19988595 1
 
< 0.1%
19987998 1
 
< 0.1%
19984655 1
 
< 0.1%
19984496 1
 
< 0.1%
19983345 1
 
< 0.1%
19982520 1
 
< 0.1%
19980693 1
 
< 0.1%
Other values (20020) 20020
> 99.9%
ValueCountFrequency (%)
2818 1
< 0.1%
3209 1
< 0.1%
20168 1
< 0.1%
25428 1
< 0.1%
27886 1
< 0.1%
28658 1
< 0.1%
28871 1
< 0.1%
29051 1
< 0.1%
31080 1
< 0.1%
41125 1
< 0.1%
ValueCountFrequency (%)
30580413 1
< 0.1%
30579673 1
< 0.1%
30578037 1
< 0.1%
30577727 1
< 0.1%
30576148 1
< 0.1%
30573892 1
< 0.1%
30563877 1
< 0.1%
30562689 1
< 0.1%
30562273 1
< 0.1%
30561688 1
< 0.1%

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct19503
Distinct (%)97.6%
Missing38
Missing (%)0.2%
Memory size156.6 KiB
Amsterdam
 
39
Amsterdam Appartement
 
11
Lovely apartment near Vondelpark
 
8
Apartment in Amsterdam
 
7
Spacious apartment with garden
 
6
Other values (19498)
19921 

Length

Max length122
Median length86
Mean length37.887855
Min length1

Characters and Unicode

Total characters757454
Distinct characters182
Distinct categories20 ?
Distinct scripts7 ?
Distinct blocks14 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19174 ?
Unique (%)95.9%

Sample

1st rowQuiet Garden View Room & Super Fast WiFi
2nd rowQuiet apt near center, great view
3rd row100%Centre-Studio 1 Private Floor/Bathroom
4th rowLovely apt in City Centre (Jordaan)
5th rowRomantic, stylish B&B houseboat in canal district

Common Values

ValueCountFrequency (%)
Amsterdam 39
 
0.2%
Amsterdam Appartement 11
 
0.1%
Lovely apartment near Vondelpark 8
 
< 0.1%
Apartment in Amsterdam 7
 
< 0.1%
Spacious apartment with garden 6
 
< 0.1%
Beautiful apartment in Amsterdam 6
 
< 0.1%
Beautiful apartment near Vondelpark 6
 
< 0.1%
Amsterdam Apartment 6
 
< 0.1%
Spacious apartment in Amsterdam 6
 
< 0.1%
Cosy apartment in Amsterdam West 5
 
< 0.1%
Other values (19493) 19892
99.3%
(Missing) 38
 
0.2%

Length

2023-05-04T22:42:16.662773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apartment 7792
 
6.5%
in 6599
 
5.5%
amsterdam 4527
 
3.8%
3924
 
3.3%
with 2991
 
2.5%
the 2436
 
2.0%
spacious 2293
 
1.9%
city 2068
 
1.7%
room 1890
 
1.6%
centre 1860
 
1.6%
Other values (5172) 82662
69.4%

Most occurring characters

ValueCountFrequency (%)
99532
 
13.1%
e 65900
 
8.7%
t 61721
 
8.1%
a 58168
 
7.7%
r 48161
 
6.4%
n 43803
 
5.8%
o 40023
 
5.3%
i 36552
 
4.8%
m 30294
 
4.0%
s 24205
 
3.2%
Other values (172) 249095
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 570345
75.3%
Space Separator 99536
 
13.1%
Uppercase Letter 64139
 
8.5%
Other Punctuation 11726
 
1.5%
Decimal Number 6389
 
0.8%
Dash Punctuation 1905
 
0.3%
Math Symbol 1509
 
0.2%
Close Punctuation 760
 
0.1%
Open Punctuation 733
 
0.1%
Other Symbol 224
 
< 0.1%
Other values (10) 188
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
 
4.7%
2
 
4.7%
2
 
4.7%
1
 
2.3%
1
 
2.3%
1
 
2.3%
1
 
2.3%
1
 
2.3%
1
 
2.3%
1
 
2.3%
Other values (30) 30
69.8%
Lowercase Letter
ValueCountFrequency (%)
e 65900
11.6%
t 61721
10.8%
a 58168
10.2%
r 48161
 
8.4%
n 43803
 
7.7%
o 40023
 
7.0%
i 36552
 
6.4%
m 30294
 
5.3%
s 24205
 
4.2%
p 21598
 
3.8%
Other values (23) 139920
24.5%
Uppercase Letter
ValueCountFrequency (%)
A 10601
16.5%
C 7461
 
11.6%
S 5049
 
7.9%
B 3724
 
5.8%
L 3705
 
5.8%
R 3353
 
5.2%
P 3222
 
5.0%
E 2943
 
4.6%
T 2695
 
4.2%
N 2596
 
4.0%
Other values (19) 18790
29.3%
Other Symbol
ValueCountFrequency (%)
152
67.9%
24
 
10.7%
18
 
8.0%
7
 
3.1%
4
 
1.8%
3
 
1.3%
2
 
0.9%
° 2
 
0.9%
2
 
0.9%
1
 
0.4%
Other values (9) 9
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 3059
26.1%
! 2581
22.0%
& 2003
17.1%
. 1442
12.3%
' 878
 
7.5%
/ 658
 
5.6%
@ 370
 
3.2%
" 274
 
2.3%
: 214
 
1.8%
* 143
 
1.2%
Other values (7) 104
 
0.9%
Decimal Number
ValueCountFrequency (%)
2 2227
34.9%
1 1146
17.9%
0 877
 
13.7%
5 551
 
8.6%
3 491
 
7.7%
4 465
 
7.3%
9 170
 
2.7%
8 155
 
2.4%
7 154
 
2.4%
6 153
 
2.4%
Math Symbol
ValueCountFrequency (%)
| 802
53.1%
+ 677
44.9%
~ 10
 
0.7%
< 9
 
0.6%
= 4
 
0.3%
> 3
 
0.2%
± 2
 
0.1%
1
 
0.1%
1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 752
98.9%
] 7
 
0.9%
1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 725
98.9%
[ 7
 
1.0%
1
 
0.1%
Modifier Symbol
ValueCountFrequency (%)
` 3
37.5%
˜ 3
37.5%
´ 2
25.0%
Space Separator
ValueCountFrequency (%)
99532
> 99.9%
  4
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1900
99.7%
5
 
0.3%
Final Punctuation
ValueCountFrequency (%)
44
86.3%
7
 
13.7%
Nonspacing Mark
ValueCountFrequency (%)
18
94.7%
1
 
5.3%
Initial Punctuation
ValueCountFrequency (%)
13
65.0%
7
35.0%
Currency Symbol
ValueCountFrequency (%)
1
50.0%
$ 1
50.0%
Other Number
ValueCountFrequency (%)
² 22
100.0%
Control
ValueCountFrequency (%)
18
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Modifier Letter
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 634482
83.8%
Common 122908
 
16.2%
Han 34
 
< 0.1%
Inherited 19
 
< 0.1%
Katakana 7
 
< 0.1%
Cyrillic 2
 
< 0.1%
Hiragana 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
99532
81.0%
, 3059
 
2.5%
! 2581
 
2.1%
2 2227
 
1.8%
& 2003
 
1.6%
- 1900
 
1.5%
. 1442
 
1.2%
1 1146
 
0.9%
' 878
 
0.7%
0 877
 
0.7%
Other values (68) 7263
 
5.9%
Latin
ValueCountFrequency (%)
e 65900
 
10.4%
t 61721
 
9.7%
a 58168
 
9.2%
r 48161
 
7.6%
n 43803
 
6.9%
o 40023
 
6.3%
i 36552
 
5.8%
m 30294
 
4.8%
s 24205
 
3.8%
p 21598
 
3.4%
Other values (50) 204057
32.2%
Han
ValueCountFrequency (%)
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (22) 22
64.7%
Katakana
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Inherited
ValueCountFrequency (%)
18
94.7%
1
 
5.3%
Cyrillic
ValueCountFrequency (%)
м 1
50.0%
А 1
50.0%
Hiragana
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 756980
99.9%
Misc Symbols 191
 
< 0.1%
Punctuation 99
 
< 0.1%
None 92
 
< 0.1%
CJK 34
 
< 0.1%
Dingbats 24
 
< 0.1%
VS 19
 
< 0.1%
Modifier Letters 3
 
< 0.1%
Geometric Shapes 3
 
< 0.1%
Misc Technical 3
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
99532
13.1%
e 65900
 
8.7%
t 61721
 
8.2%
a 58168
 
7.7%
r 48161
 
6.4%
n 43803
 
5.8%
o 40023
 
5.3%
i 36552
 
4.8%
m 30294
 
4.0%
s 24205
 
3.2%
Other values (82) 248621
32.8%
Misc Symbols
ValueCountFrequency (%)
152
79.6%
24
 
12.6%
7
 
3.7%
4
 
2.1%
1
 
0.5%
1
 
0.5%
1
 
0.5%
1
 
0.5%
Punctuation
ValueCountFrequency (%)
44
44.4%
23
23.2%
13
 
13.1%
7
 
7.1%
7
 
7.1%
5
 
5.1%
None
ValueCountFrequency (%)
é 27
29.3%
² 22
23.9%
É 4
 
4.3%
  4
 
4.3%
· 4
 
4.3%
à 3
 
3.3%
2
 
2.2%
° 2
 
2.2%
ü 2
 
2.2%
´ 2
 
2.2%
Other values (16) 20
21.7%
VS
ValueCountFrequency (%)
18
94.7%
1
 
5.3%
Dingbats
ValueCountFrequency (%)
18
75.0%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Modifier Letters
ValueCountFrequency (%)
˜ 3
100.0%
Geometric Shapes
ValueCountFrequency (%)
3
100.0%
CJK
ValueCountFrequency (%)
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (22) 22
64.7%
Misc Technical
ValueCountFrequency (%)
2
66.7%
1
33.3%
Cyrillic
ValueCountFrequency (%)
м 1
50.0%
А 1
50.0%
Hiragana
ValueCountFrequency (%)
1
50.0%
1
50.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

host_id
Real number (ℝ)

Distinct17264
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48685695
Minimum3159
Maximum2.2936124 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:16.851868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3159
5-th percentile1725242
Q18093515.8
median23694499
Q368275352
95-th percentile1.8463359 × 108
Maximum2.2936124 × 108
Range2.2935808 × 108
Interquartile range (IQR)60181836

Descriptive statistics

Standard deviation56496353
Coefficient of variation (CV)1.1604302
Kurtosis1.4522264
Mean48685695
Median Absolute Deviation (MAD)19349992
Skewness1.5226291
Sum9.7517448 × 1011
Variance3.1918379 × 1015
MonotonicityNot monotonic
2023-05-04T22:42:17.028882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65859990 208
 
1.0%
1464510 105
 
0.5%
76104209 83
 
0.4%
113977564 38
 
0.2%
517215 30
 
0.1%
107745142 27
 
0.1%
14183886 25
 
0.1%
11969034 24
 
0.1%
44168250 21
 
0.1%
7594884 21
 
0.1%
Other values (17254) 19448
97.1%
ValueCountFrequency (%)
3159 1
< 0.1%
3806 1
< 0.1%
5988 1
< 0.1%
12085 1
< 0.1%
20405 1
< 0.1%
30390 1
< 0.1%
34080 1
< 0.1%
36701 1
< 0.1%
47517 1
< 0.1%
49851 1
< 0.1%
ValueCountFrequency (%)
229361236 1
 
< 0.1%
229295710 1
 
< 0.1%
229260989 1
 
< 0.1%
229196646 5
< 0.1%
229109698 1
 
< 0.1%
229053056 4
< 0.1%
229026227 1
 
< 0.1%
229023758 1
 
< 0.1%
228892275 1
 
< 0.1%
228856945 1
 
< 0.1%

host_name
Categorical

Distinct5915
Distinct (%)29.5%
Missing4
Missing (%)< 0.1%
Memory size156.6 KiB
Martijn
 
289
Michiel And Jane
 
105
Laura
 
98
Anne
 
90
Marieke
 
88
Other values (5910)
19356 

Length

Max length34
Median length30
Mean length6.2987616
Min length1

Characters and Unicode

Total characters126139
Distinct characters103
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3662 ?
Unique (%)18.3%

Sample

1st rowDaniel
2nd rowMaartje
3rd rowAlex
4th rowJoan
5th rowFlip

Common Values

ValueCountFrequency (%)
Martijn 289
 
1.4%
Michiel And Jane 105
 
0.5%
Laura 98
 
0.5%
Anne 90
 
0.4%
Marieke 88
 
0.4%
Eva 88
 
0.4%
Willem 87
 
0.4%
Rated 83
 
0.4%
Thomas 80
 
0.4%
David 77
 
0.4%
Other values (5905) 18941
94.6%

Length

2023-05-04T22:42:17.210761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
715
 
3.1%
martijn 296
 
1.3%
and 234
 
1.0%
michiel 183
 
0.8%
en 137
 
0.6%
jan 125
 
0.5%
jane 110
 
0.5%
laura 109
 
0.5%
willem 105
 
0.5%
anne 104
 
0.5%
Other values (5299) 20786
90.8%

Most occurring characters

ValueCountFrequency (%)
a 14096
 
11.2%
e 14076
 
11.2%
i 10788
 
8.6%
n 10549
 
8.4%
r 8367
 
6.6%
l 5733
 
4.5%
o 5579
 
4.4%
t 4821
 
3.8%
s 4089
 
3.2%
M 3150
 
2.5%
Other values (93) 44891
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 99519
78.9%
Uppercase Letter 22532
 
17.9%
Space Separator 2888
 
2.3%
Other Punctuation 913
 
0.7%
Dash Punctuation 155
 
0.1%
Open Punctuation 50
 
< 0.1%
Close Punctuation 50
 
< 0.1%
Decimal Number 12
 
< 0.1%
Math Symbol 10
 
< 0.1%
Other Symbol 6
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14096
14.2%
e 14076
14.1%
i 10788
10.8%
n 10549
10.6%
r 8367
8.4%
l 5733
 
5.8%
o 5579
 
5.6%
t 4821
 
4.8%
s 4089
 
4.1%
u 2569
 
2.6%
Other values (42) 18852
18.9%
Uppercase Letter
ValueCountFrequency (%)
M 3150
14.0%
J 2136
 
9.5%
A 1893
 
8.4%
S 1752
 
7.8%
R 1429
 
6.3%
E 1263
 
5.6%
L 1253
 
5.6%
C 952
 
4.2%
D 949
 
4.2%
B 888
 
3.9%
Other values (20) 6867
30.5%
Other Punctuation
ValueCountFrequency (%)
& 768
84.1%
. 95
 
10.4%
, 35
 
3.8%
' 8
 
0.9%
/ 6
 
0.7%
@ 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 6
50.0%
5 2
 
16.7%
3 2
 
16.7%
2 1
 
8.3%
9 1
 
8.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
2888
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 155
100.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%
Other Symbol
ValueCountFrequency (%)
6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 122045
96.8%
Common 4086
 
3.2%
Cyrillic 6
 
< 0.1%
Han 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14096
 
11.5%
e 14076
 
11.5%
i 10788
 
8.8%
n 10549
 
8.6%
r 8367
 
6.9%
l 5733
 
4.7%
o 5579
 
4.6%
t 4821
 
4.0%
s 4089
 
3.4%
M 3150
 
2.6%
Other values (67) 40797
33.4%
Common
ValueCountFrequency (%)
2888
70.7%
& 768
 
18.8%
- 155
 
3.8%
. 95
 
2.3%
( 50
 
1.2%
) 50
 
1.2%
, 35
 
0.9%
+ 10
 
0.2%
' 8
 
0.2%
6
 
0.1%
Other values (9) 21
 
0.5%
Cyrillic
ValueCountFrequency (%)
а 2
33.3%
с 1
16.7%
О 1
16.7%
к 1
16.7%
н 1
16.7%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125923
99.8%
None 202
 
0.2%
Misc Symbols 6
 
< 0.1%
Cyrillic 6
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14096
 
11.2%
e 14076
 
11.2%
i 10788
 
8.6%
n 10549
 
8.4%
r 8367
 
6.6%
l 5733
 
4.6%
o 5579
 
4.4%
t 4821
 
3.8%
s 4089
 
3.2%
M 3150
 
2.5%
Other values (59) 44675
35.5%
None
ValueCountFrequency (%)
é 83
41.1%
ë 34
16.8%
è 19
 
9.4%
í 10
 
5.0%
ç 8
 
4.0%
ï 8
 
4.0%
á 7
 
3.5%
ö 4
 
2.0%
ü 4
 
2.0%
ı 3
 
1.5%
Other values (16) 22
 
10.9%
Misc Symbols
ValueCountFrequency (%)
6
100.0%
Cyrillic
ValueCountFrequency (%)
а 2
33.3%
с 1
16.7%
О 1
16.7%
к 1
16.7%
н 1
16.7%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

neighbourhood_group
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing20030
Missing (%)100.0%
Memory size156.6 KiB

neighbourhood
Categorical

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.6 KiB
De Baarsjes - Oud-West
3515 
De Pijp - Rivierenbuurt
2493 
Centrum-West
2326 
Centrum-Oost
1730 
Westerpark
1490 
Other values (17)
8476 

Length

Max length38
Median length23
Mean length15.978283
Min length4

Characters and Unicode

Total characters320045
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOostelijk Havengebied - Indische Buurt
2nd rowWesterpark
3rd rowCentrum-Oost
4th rowCentrum-West
5th rowCentrum-West

Common Values

ValueCountFrequency (%)
De Baarsjes - Oud-West 3515
17.5%
De Pijp - Rivierenbuurt 2493
12.4%
Centrum-West 2326
11.6%
Centrum-Oost 1730
8.6%
Westerpark 1490
7.4%
Zuid 1441
7.2%
Oud-Oost 1282
 
6.4%
Bos en Lommer 1145
 
5.7%
Oostelijk Havengebied - Indische Buurt 959
 
4.8%
Oud-Noord 571
 
2.9%
Other values (12) 3078
15.4%

Length

2023-05-04T22:42:17.385050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8157
17.4%
de 6150
13.1%
baarsjes 3515
 
7.5%
oud-west 3515
 
7.5%
pijp 2493
 
5.3%
rivierenbuurt 2493
 
5.3%
centrum-west 2326
 
5.0%
centrum-oost 1730
 
3.7%
westerpark 1490
 
3.2%
zuid 1441
 
3.1%
Other values (27) 13534
28.9%

Most occurring characters

ValueCountFrequency (%)
e 41836
 
13.1%
26814
 
8.4%
r 24136
 
7.5%
s 22204
 
6.9%
t 21810
 
6.8%
u 19662
 
6.1%
- 18365
 
5.7%
i 13244
 
4.1%
a 13001
 
4.1%
d 11466
 
3.6%
Other values (31) 107507
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 226664
70.8%
Uppercase Letter 48202
 
15.1%
Space Separator 26814
 
8.4%
Dash Punctuation 18365
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 41836
18.5%
r 24136
10.6%
s 22204
9.8%
t 21810
9.6%
u 19662
8.7%
i 13244
 
5.8%
a 13001
 
5.7%
d 11466
 
5.1%
n 10913
 
4.8%
o 9938
 
4.4%
Other values (13) 38454
17.0%
Uppercase Letter
ValueCountFrequency (%)
O 9851
20.4%
W 8203
17.0%
D 6272
13.0%
B 6088
12.6%
C 4167
8.6%
P 2493
 
5.2%
R 2493
 
5.2%
Z 2155
 
4.5%
I 1411
 
2.9%
N 1290
 
2.7%
Other values (6) 3779
 
7.8%
Space Separator
ValueCountFrequency (%)
26814
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18365
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 274866
85.9%
Common 45179
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 41836
15.2%
r 24136
 
8.8%
s 22204
 
8.1%
t 21810
 
7.9%
u 19662
 
7.2%
i 13244
 
4.8%
a 13001
 
4.7%
d 11466
 
4.2%
n 10913
 
4.0%
o 9938
 
3.6%
Other values (29) 86656
31.5%
Common
ValueCountFrequency (%)
26814
59.4%
- 18365
40.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 320045
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 41836
 
13.1%
26814
 
8.4%
r 24136
 
7.5%
s 22204
 
6.9%
t 21810
 
6.8%
u 19662
 
6.1%
- 18365
 
5.7%
i 13244
 
4.1%
a 13001
 
4.1%
d 11466
 
3.6%
Other values (31) 107507
33.6%

latitude
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct20030
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.365212
Minimum52.288378
Maximum52.424713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:17.590633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum52.288378
5-th percentile52.342687
Q152.355126
median52.36459
Q352.375074
95-th percentile52.39005
Maximum52.424713
Range0.13633432
Interquartile range (IQR)0.019948024

Descriptive statistics

Standard deviation0.015995991
Coefficient of variation (CV)0.0003054698
Kurtosis2.1181302
Mean52.365212
Median Absolute Deviation (MAD)0.0098915095
Skewness-0.1977422
Sum1048875.2
Variance0.00025587173
MonotonicityNot monotonic
2023-05-04T22:42:17.778183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.36575451 1
 
< 0.1%
52.37934236 1
 
< 0.1%
52.34363541 1
 
< 0.1%
52.37879082 1
 
< 0.1%
52.35459348 1
 
< 0.1%
52.38561195 1
 
< 0.1%
52.37284509 1
 
< 0.1%
52.36991662 1
 
< 0.1%
52.35877933 1
 
< 0.1%
52.3961998 1
 
< 0.1%
Other values (20020) 20020
> 99.9%
ValueCountFrequency (%)
52.28837824 1
< 0.1%
52.28927442 1
< 0.1%
52.29030764 1
< 0.1%
52.29068744 1
< 0.1%
52.29109538 1
< 0.1%
52.29164386 1
< 0.1%
52.29178467 1
< 0.1%
52.29220885 1
< 0.1%
52.29277879 1
< 0.1%
52.29281904 1
< 0.1%
ValueCountFrequency (%)
52.42471256 1
< 0.1%
52.42464056 1
< 0.1%
52.42399339 1
< 0.1%
52.42380134 1
< 0.1%
52.42364739 1
< 0.1%
52.42350166 1
< 0.1%
52.4234977 1
< 0.1%
52.42343234 1
< 0.1%
52.42336689 1
< 0.1%
52.42332129 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct20030
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8889774
Minimum4.7532466
Maximum5.027689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:17.959944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.7532466
5-th percentile4.8445341
Q14.8635951
median4.8863869
Q34.9082884
95-th percentile4.9460886
Maximum5.027689
Range0.27444239
Interquartile range (IQR)0.044693385

Descriptive statistics

Standard deviation0.035573429
Coefficient of variation (CV)0.0072762515
Kurtosis1.1442911
Mean4.8889774
Median Absolute Deviation (MAD)0.022407105
Skewness0.55247624
Sum97926.216
Variance0.0012654688
MonotonicityNot monotonic
2023-05-04T22:42:18.130534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.941419235 1
 
< 0.1%
4.926475131 1
 
< 0.1%
4.925121865 1
 
< 0.1%
4.859313343 1
 
< 0.1%
4.923211504 1
 
< 0.1%
4.883201766 1
 
< 0.1%
4.894525181 1
 
< 0.1%
4.92743154 1
 
< 0.1%
4.920524682 1
 
< 0.1%
4.93293638 1
 
< 0.1%
Other values (20020) 20020
> 99.9%
ValueCountFrequency (%)
4.753246567 1
< 0.1%
4.759434181 1
< 0.1%
4.763264201 1
< 0.1%
4.765050687 1
< 0.1%
4.768451848 1
< 0.1%
4.772158795 1
< 0.1%
4.773175277 1
< 0.1%
4.775167687 1
< 0.1%
4.775504535 1
< 0.1%
4.775947779 1
< 0.1%
ValueCountFrequency (%)
5.027688954 1
< 0.1%
5.026700926 1
< 0.1%
5.018401449 1
< 0.1%
5.018302533 1
< 0.1%
5.018060461 1
< 0.1%
5.017531443 1
< 0.1%
5.016975529 1
< 0.1%
5.013616381 1
< 0.1%
5.01355728 1
< 0.1%
5.013534319 1
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.6 KiB
Entire home/apt
15889 
Private room
4076 
Shared room
 
65

Length

Max length15
Median length15
Mean length14.376535
Min length11

Characters and Unicode

Total characters287962
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 15889
79.3%
Private room 4076
 
20.3%
Shared room 65
 
0.3%

Length

2023-05-04T22:42:18.536951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-04T22:42:18.728469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 15889
39.7%
home/apt 15889
39.7%
room 4141
 
10.3%
private 4076
 
10.2%
shared 65
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 35919
12.5%
t 35854
12.5%
o 24171
8.4%
r 24171
8.4%
a 20030
 
7.0%
20030
 
7.0%
m 20030
 
7.0%
i 19965
 
6.9%
h 15954
 
5.5%
p 15889
 
5.5%
Other values (7) 55949
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 232013
80.6%
Space Separator 20030
 
7.0%
Uppercase Letter 20030
 
7.0%
Other Punctuation 15889
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 35919
15.5%
t 35854
15.5%
o 24171
10.4%
r 24171
10.4%
a 20030
8.6%
m 20030
8.6%
i 19965
8.6%
h 15954
6.9%
p 15889
6.8%
n 15889
6.8%
Other values (2) 4141
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
E 15889
79.3%
P 4076
 
20.3%
S 65
 
0.3%
Space Separator
ValueCountFrequency (%)
20030
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 15889
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 252043
87.5%
Common 35919
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 35919
14.3%
t 35854
14.2%
o 24171
9.6%
r 24171
9.6%
a 20030
7.9%
m 20030
7.9%
i 19965
7.9%
h 15954
6.3%
p 15889
6.3%
E 15889
6.3%
Other values (5) 24171
9.6%
Common
ValueCountFrequency (%)
20030
55.8%
/ 15889
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 35919
12.5%
t 35854
12.5%
o 24171
8.4%
r 24171
8.4%
a 20030
 
7.0%
20030
 
7.0%
m 20030
 
7.0%
i 19965
 
6.9%
h 15954
 
5.5%
p 15889
 
5.5%
Other values (7) 55949
19.4%

price
Real number (ℝ)

Distinct429
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.18118
Minimum0
Maximum8500
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:18.911801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q196
median125
Q3175
95-th percentile300
Maximum8500
Range8500
Interquartile range (IQR)79

Descriptive statistics

Standard deviation145.82898
Coefficient of variation (CV)0.95825895
Kurtosis1168.155
Mean152.18118
Median Absolute Deviation (MAD)35
Skewness25.447702
Sum3048189
Variance21266.09
MonotonicityNot monotonic
2023-05-04T22:42:19.107782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1209
 
6.0%
100 1117
 
5.6%
120 961
 
4.8%
200 667
 
3.3%
125 656
 
3.3%
110 563
 
2.8%
175 540
 
2.7%
90 531
 
2.7%
80 527
 
2.6%
130 525
 
2.6%
Other values (419) 12734
63.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
19 1
 
< 0.1%
20 2
 
< 0.1%
23 2
 
< 0.1%
24 1
 
< 0.1%
25 6
< 0.1%
26 4
< 0.1%
ValueCountFrequency (%)
8500 1
< 0.1%
8000 1
< 0.1%
5040 1
< 0.1%
5000 1
< 0.1%
4500 1
< 0.1%
3900 1
< 0.1%
3142 1
< 0.1%
3000 1
< 0.1%
2500 2
< 0.1%
2200 1
< 0.1%

minimum_nights
Real number (ℝ)

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3287069
Minimum1
Maximum1001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:19.316804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile7
Maximum1001
Range1000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.537419
Coefficient of variation (CV)3.7664532
Kurtosis4130.872
Mean3.3287069
Median Absolute Deviation (MAD)1
Skewness56.361392
Sum66674
Variance157.18687
MonotonicityNot monotonic
2023-05-04T22:42:19.515615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 8104
40.5%
3 4965
24.8%
1 3128
 
15.6%
4 1555
 
7.8%
5 982
 
4.9%
7 555
 
2.8%
6 243
 
1.2%
14 90
 
0.4%
10 82
 
0.4%
30 61
 
0.3%
Other values (41) 265
 
1.3%
ValueCountFrequency (%)
1 3128
 
15.6%
2 8104
40.5%
3 4965
24.8%
4 1555
 
7.8%
5 982
 
4.9%
6 243
 
1.2%
7 555
 
2.8%
8 30
 
0.1%
9 17
 
0.1%
10 82
 
0.4%
ValueCountFrequency (%)
1001 1
 
< 0.1%
999 1
 
< 0.1%
365 3
< 0.1%
300 1
 
< 0.1%
230 1
 
< 0.1%
222 1
 
< 0.1%
186 1
 
< 0.1%
185 1
 
< 0.1%
183 1
 
< 0.1%
181 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct354
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.560459
Minimum0
Maximum695
Zeros2388
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:19.721585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q322
95-th percentile85
Maximum695
Range695
Interquartile range (IQR)19

Descriptive statistics

Standard deviation43.240292
Coefficient of variation (CV)2.0055367
Kurtosis41.501917
Mean21.560459
Median Absolute Deviation (MAD)7
Skewness5.4150261
Sum431856
Variance1869.7228
MonotonicityNot monotonic
2023-05-04T22:42:19.935428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2388
 
11.9%
1 1408
 
7.0%
2 1198
 
6.0%
3 1071
 
5.3%
4 956
 
4.8%
5 857
 
4.3%
6 779
 
3.9%
7 725
 
3.6%
8 651
 
3.3%
9 586
 
2.9%
Other values (344) 9411
47.0%
ValueCountFrequency (%)
0 2388
11.9%
1 1408
7.0%
2 1198
6.0%
3 1071
5.3%
4 956
4.8%
5 857
 
4.3%
6 779
 
3.9%
7 725
 
3.6%
8 651
 
3.3%
9 586
 
2.9%
ValueCountFrequency (%)
695 1
< 0.1%
631 1
< 0.1%
606 1
< 0.1%
602 1
< 0.1%
580 1
< 0.1%
576 1
< 0.1%
574 1
< 0.1%
541 1
< 0.1%
540 1
< 0.1%
527 1
< 0.1%

last_review
Categorical

HIGH CARDINALITY  MISSING 

Distinct1202
Distinct (%)6.8%
Missing2406
Missing (%)12.0%
Memory size156.6 KiB
2018-12-02
 
492
2018-11-18
 
472
2018-11-25
 
457
2018-10-21
 
350
2018-11-04
 
345
Other values (1197)
15508 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters176240
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique264 ?
Unique (%)1.5%

Sample

1st row2018-11-28
2nd row2018-08-29
3rd row2018-11-30
4th row2018-01-21
5th row2018-11-25

Common Values

ValueCountFrequency (%)
2018-12-02 492
 
2.5%
2018-11-18 472
 
2.4%
2018-11-25 457
 
2.3%
2018-10-21 350
 
1.7%
2018-11-04 345
 
1.7%
2018-10-22 279
 
1.4%
2018-12-03 270
 
1.3%
2018-11-19 235
 
1.2%
2018-11-11 230
 
1.1%
2018-10-28 221
 
1.1%
Other values (1192) 14273
71.3%
(Missing) 2406
 
12.0%

Length

2023-05-04T22:42:20.139808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-12-02 492
 
2.8%
2018-11-18 472
 
2.7%
2018-11-25 457
 
2.6%
2018-10-21 350
 
2.0%
2018-11-04 345
 
2.0%
2018-10-22 279
 
1.6%
2018-12-03 270
 
1.5%
2018-11-19 235
 
1.3%
2018-11-11 230
 
1.3%
2018-10-28 221
 
1.3%
Other values (1192) 14273
81.0%

Most occurring characters

ValueCountFrequency (%)
1 37787
21.4%
0 36612
20.8%
- 35248
20.0%
2 27701
15.7%
8 17594
10.0%
7 5646
 
3.2%
6 3669
 
2.1%
9 3625
 
2.1%
5 3088
 
1.8%
3 2843
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140992
80.0%
Dash Punctuation 35248
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37787
26.8%
0 36612
26.0%
2 27701
19.6%
8 17594
12.5%
7 5646
 
4.0%
6 3669
 
2.6%
9 3625
 
2.6%
5 3088
 
2.2%
3 2843
 
2.0%
4 2427
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 35248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37787
21.4%
0 36612
20.8%
- 35248
20.0%
2 27701
15.7%
8 17594
10.0%
7 5646
 
3.2%
6 3669
 
2.1%
9 3625
 
2.1%
5 3088
 
1.8%
3 2843
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37787
21.4%
0 36612
20.8%
- 35248
20.0%
2 27701
15.7%
8 17594
10.0%
7 5646
 
3.2%
6 3669
 
2.1%
9 3625
 
2.1%
5 3088
 
1.8%
3 2843
 
1.6%

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct754
Distinct (%)4.3%
Missing2406
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean1.0620801
Minimum0.01
Maximum11.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:20.333442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q10.27
median0.62
Q31.23
95-th percentile4
Maximum11.85
Range11.84
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation1.3333201
Coefficient of variation (CV)1.2553856
Kurtosis9.2001935
Mean1.0620801
Median Absolute Deviation (MAD)0.41
Skewness2.7343685
Sum18718.1
Variance1.7777425
MonotonicityNot monotonic
2023-05-04T22:42:20.603379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 239
 
1.2%
0.07 238
 
1.2%
1 236
 
1.2%
0.25 211
 
1.1%
0.06 208
 
1.0%
0.18 205
 
1.0%
0.27 199
 
1.0%
0.17 193
 
1.0%
0.1 192
 
1.0%
0.2 181
 
0.9%
Other values (744) 15522
77.5%
(Missing) 2406
 
12.0%
ValueCountFrequency (%)
0.01 5
 
< 0.1%
0.02 75
 
0.4%
0.03 239
1.2%
0.04 163
0.8%
0.05 153
0.8%
0.06 208
1.0%
0.07 238
1.2%
0.08 145
0.7%
0.09 157
0.8%
0.1 192
1.0%
ValueCountFrequency (%)
11.85 1
< 0.1%
11.58 1
< 0.1%
11.44 1
< 0.1%
11.09 1
< 0.1%
10.96 1
< 0.1%
10.86 1
< 0.1%
10.68 1
< 0.1%
10.15 1
< 0.1%
10.12 1
< 0.1%
10.01 1
< 0.1%
Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.734698
Minimum1
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:20.912289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum208
Range207
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.921886
Coefficient of variation (CV)4.8412563
Kurtosis63.73466
Mean4.734698
Median Absolute Deviation (MAD)0
Skewness7.8549484
Sum94836
Variance525.41286
MonotonicityNot monotonic
2023-05-04T22:42:21.128845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 15830
79.0%
2 2208
 
11.0%
3 522
 
2.6%
208 208
 
1.0%
4 200
 
1.0%
5 185
 
0.9%
6 144
 
0.7%
105 105
 
0.5%
83 83
 
0.4%
7 63
 
0.3%
Other values (17) 482
 
2.4%
ValueCountFrequency (%)
1 15830
79.0%
2 2208
 
11.0%
3 522
 
2.6%
4 200
 
1.0%
5 185
 
0.9%
6 144
 
0.7%
7 63
 
0.3%
8 48
 
0.2%
9 36
 
0.2%
10 30
 
0.1%
ValueCountFrequency (%)
208 208
1.0%
105 105
0.5%
83 83
 
0.4%
38 38
 
0.2%
30 30
 
0.1%
27 27
 
0.1%
25 25
 
0.1%
24 24
 
0.1%
21 42
 
0.2%
19 38
 
0.2%

availability_365
Real number (ℝ)

Distinct366
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.913679
Minimum0
Maximum365
Zeros9344
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size156.6 KiB
2023-05-04T22:42:21.339520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q367
95-th percentile336
Maximum365
Range365
Interquartile range (IQR)67

Descriptive statistics

Standard deviation104.02771
Coefficient of variation (CV)1.7362931
Kurtosis1.9994437
Mean59.913679
Median Absolute Deviation (MAD)3
Skewness1.8194251
Sum1200071
Variance10821.765
MonotonicityNot monotonic
2023-05-04T22:42:21.565861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9344
46.7%
5 245
 
1.2%
3 244
 
1.2%
4 241
 
1.2%
7 239
 
1.2%
2 235
 
1.2%
6 228
 
1.1%
1 225
 
1.1%
8 213
 
1.1%
9 196
 
1.0%
Other values (356) 8620
43.0%
ValueCountFrequency (%)
0 9344
46.7%
1 225
 
1.1%
2 235
 
1.2%
3 244
 
1.2%
4 241
 
1.2%
5 245
 
1.2%
6 228
 
1.1%
7 239
 
1.2%
8 213
 
1.1%
9 196
 
1.0%
ValueCountFrequency (%)
365 66
0.3%
364 162
0.8%
363 46
 
0.2%
362 44
 
0.2%
361 39
 
0.2%
360 28
 
0.1%
359 30
 
0.1%
358 42
 
0.2%
357 40
 
0.2%
356 28
 
0.1%

Interactions

2023-05-04T22:42:13.499266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:41:58.844931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:00.786131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:02.320889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:03.870979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:05.600152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:07.079388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:08.573904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:10.397104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:11.969559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:13.653196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:41:59.001235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:00.943816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:02.477945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:04.027830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:05.750501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:07.231052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:08.736146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:10.555098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:12.124227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:13.803671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:41:59.149041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:01.097084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:02.624967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:04.171715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:05.891517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:07.377497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:08.896336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:10.702940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:12.265636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:13.963656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:41:59.315037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:01.254568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:02.783673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:04.330467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:06.057781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:07.532538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:09.056042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:10.863967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:12.435921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:14.319933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:41:59.470018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:01.402731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:02.938669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:04.489977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:06.203944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:07.680023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:09.212292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:11.004812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:12.582400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:14.470389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:41:59.873375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:01.560313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:03.085573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:04.842113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:06.342654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:07.820414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:09.565285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:11.149157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:12.725870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:14.622827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:00.067007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:01.705800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:03.238718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:04.985811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:06.484754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:07.969471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:09.720272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:11.301658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:12.871487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:14.782732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:00.227827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:01.861083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:03.396722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:05.146781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:06.641546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:08.123116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:09.881857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:11.484397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:13.033088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:14.939394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:00.384303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:02.013574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:03.553304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:05.292957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:06.783974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:08.277168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:10.044248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:11.645349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:13.185693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:15.086037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:00.607634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:02.155029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:03.703366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:05.441450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:06.927895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:08.414414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:10.219643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:11.799243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T22:42:13.335767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-05-04T22:42:21.786988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
idhost_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365neighbourhoodroom_type
id1.0000.463-0.0100.0040.026-0.120-0.4530.2690.1250.0670.0390.054
host_id0.4631.000-0.0180.036-0.023-0.139-0.2000.1230.028-0.0050.0550.061
latitude-0.010-0.0181.000-0.1270.0000.0020.0670.0620.0380.0270.6790.079
longitude0.0040.036-0.1271.0000.0470.0170.0050.0160.0110.0270.6640.095
price0.026-0.0230.0000.0471.0000.116-0.065-0.054-0.0320.1490.0070.000
minimum_nights-0.120-0.1390.0020.0170.1161.000-0.080-0.206-0.079-0.0030.0060.000
number_of_reviews-0.453-0.2000.0670.005-0.065-0.0801.0000.6500.0130.2600.0330.226
reviews_per_month0.2690.1230.0620.016-0.054-0.2060.6501.0000.1600.4210.0640.316
calculated_host_listings_count0.1250.0280.0380.011-0.032-0.0790.0130.1601.0000.2040.0420.053
availability_3650.067-0.0050.0270.0270.149-0.0030.2600.4210.2041.0000.0670.167
neighbourhood0.0390.0550.6790.6640.0070.0060.0330.0640.0420.0671.0000.139
room_type0.0540.0610.0790.0950.0000.0000.2260.3160.0530.1670.1391.000

Missing values

2023-05-04T22:42:15.328508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-04T22:42:15.653408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-04T22:42:15.963519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02818Quiet Garden View Room & Super Fast WiFi3159DanielNaNOostelijk Havengebied - Indische Buurt52.3657554.941419Private room5932482018-11-282.10144
13209Quiet apt near center, great view3806MaartjeNaNWesterpark52.3902254.873924Entire home/apt1604422018-08-291.03147
220168100%Centre-Studio 1 Private Floor/Bathroom59484AlexNaNCentrum-Oost52.3650874.893541Entire home/apt8012332018-11-302.182198
325428Lovely apt in City Centre (Jordaan)56142JoanNaNCentrum-West52.3731144.883668Entire home/apt1251412018-01-210.092141
427886Romantic, stylish B&B houseboat in canal district97647FlipNaNCentrum-West52.3867274.892078Private room15021712018-11-252.031199
528658Cosy guest room near city centre -1123414MicheleNaNBos en Lommer52.3753424.857289Private room6534342018-11-194.162295
628871Comfortable double room124245EdwinNaNCentrum-West52.3671874.890918Private room7522152018-12-032.133137
729051Comfortable single room124245EdwinNaNCentrum-West52.3677254.891512Private room5523832018-12-054.073188
8310802-story apartment + rooftop terrace133488NienkeNaNZuid52.3513214.848383Entire home/apt2193322017-10-160.361336
941125Amsterdam Center Entire Apartment178515FatihNaNCentrum-West52.3789154.883205Entire home/apt1803762018-10-070.78111
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
2002030561688Cozy apartment close to the center of Amsterdam228892275JoelNaNOsdorp52.3573654.784800Entire home/apt15010NaNNaN134
2002130562273Designer & charming apartment in Oud-Zuid853028ElisaNaNZuid52.3517664.853029Entire home/apt15020NaNNaN123
2002230562689Cosy two floor apartment with a big terrace229295710Mariana & RobNaNWatergraafsmeer52.3563274.933991Entire home/apt8050NaNNaN1234
2002330563877Large comfortable apartments in the center228749822RozaliiaNaNCentrum-Oost52.3658124.896044Entire home/apt20070NaNNaN130
2002430573892Clean and perfect location229361236PoppyNaNDe Baarsjes - Oud-West52.3639014.878162Entire home/apt8110NaNNaN1133
2002530576148Family House City + free Parking+garden (160 m2)13399651MariekeNaNWatergraafsmeer52.3459994.952145Entire home/apt34070NaNNaN111
2002630577727Home Sweet Home in Indische Buurt1595885EvitaNaNOostelijk Havengebied - Indische Buurt52.3624124.932467Entire home/apt15030NaNNaN216
2002730578037Amsterdam Cozy apartment nearby center87866499TommasoNaNOud-Oost52.3624314.926912Entire home/apt80100NaNNaN2210
2002830579673Home Sweet Home for a Guest or a Couple1595885EvitaNaNOostelijk Havengebied - Indische Buurt52.3637804.932493Private room5520NaNNaN231
2002930580413Cosy two bedroom appartment near 'de Pijp'!27212057LisaNaNDe Pijp - Rivierenbuurt52.3469114.901932Entire home/apt22050NaNNaN114